Logistic regression returns a probability. You can use the returned probability "as is" (for example, the probability that the user will click on this ad is 0.00023) or convert the returned probability to a binary value (for example, this email is spam).

A logistic regression model that returns 0.9995 for
a particular email message is predicting that it is very likely to be spam. Conversely,
another email message with a prediction score of 0.0003 on that same logistic
regression model is very likely not spam.
However, what about an email message with a prediction score of 0.6? In order
to map a logistic regression value to a binary category, you must define a
**classification threshold** (also called the **decision threshold**).
A value above that threshold indicates "spam"; a value below indicates "not spam."
It is tempting to assume that the classification threshold should always be 0.5,
but thresholds are problem-dependent, and are therefore values that you must tune.

The following sections take a closer look at metrics you can use to evaluate a classification model's predictions, as well as the impact of changing the classification threshold on these predictions.